Electroencephalographic signals (EEG) are exemplary weak physiological signals generated by electrical activity of the brain. EOG and EMG signals are similar and are generated by electrical activity, respectively, of the eye muscles or of muscles generally.
EEG recordings have a number of diagnostic and other uses. For example, as known in the art, sleep studies usually require determination of subject alertness, and because different alertness levels generate sufficiently characteristic EEG signals, subject alertness can be determined from EEG examination. EEG interpretation rules have been developed by which it can be determined that a subject is likely to be either awake, or drowsy, or sleeping, where sleeping itself can be classified as either stage I, stage II, stage III or REM. See, e.g., Rechtschaffen et al., eds., 1968, A manual of standardized terminology, techniques, and scoring system for sleep stages of human subjects. U.S. Dept. of Health, Education, and Welfare, Public Health Service.
EEG signals have a relatively broad band, routinely being measured over a bandwidth including at least the frequencies from 0.5-30 Hz or higher which is divided into sub-bands denoted, from the low to the high, as delta, theta, alpha and beta frequencies. See, e.g., Hill et al., 1963, Electroencephalography. London, McDonald. Additionally, mainly during sleep, EEG signals may include brief higher frequency bursts known as spindles, K complexes, and the like. Further, EEG signals have relatively low amplitudes, often no more than 10's of micro-volts. They are attenuated during conduction from their origin in the brain through tissue, bone and skin to their recording electrodes contacting the scalp.
These signal characteristics often lead to undesirable spurious signals and artifacts superimposed on the EEG signals of interest. See, e.g., Metting et al., 1990, High-quality recording of bioelectric events. I: interference reduction, theory and practice, Med. & Biol. Eng. & Comput., vol. 28, pp. 389-397. First, conduction from the brain to the pick-up electrodes can reduce amplitudes and add noise, leading to lower signal to noise ratios. Second, because of their low amplitude and the often high impedance of their pick-up electrodes, spurious signals and artifacts arising from non-cerebral sources are easily picked up.
Spurious signals and artifacts found in the EEG can be usually seen to be either physiologic artifacts or non-physiologic artifacts. Physiologic artifacts arise from electrical activity elsewhere in the body, primarily in the heart or skeletal muscles. Cardiac artifacts are often found in EEG signals, most prominently in subjects with short and wide necks, and can be identified as higher frequency bursts synchronous with the QRS complexes of the electrocardiogram (ECG). Further, pulse artifacts can be induced in EEG electrodes placed over pulsating vessels. They can appear as slow waves generally similar to normal EEG activity, and can be identified since they usually trail QRS complexes by approximately 200-300 milliseconds. Skeletal muscle contractions also generate broad-band, low amplitudes electrical activity that can appear in the EEG such as from power supply lines (50 Hz generally, or 60 Hz in the US), fluorescent lights and a range of electronic devices in the vicinity of a subject. Metting et al., 1990.
Spurious signals and artifacts complicate EEG interpretation. EEG signals with significant artifacts may have to be entirely discarded. Lesser artifacts may make EEG signal interpretation more difficult or even lead to mis-interpretation. Accordingly, efforts have been directed to limiting or removing such artifacts.
These efforts include physical means, such as use of shielded electrode cables to limit pickup of non-physiologic artifacts. However, widespread clinical use of shielded cables has been hampered by their expense and by their often difficult and delicate setup. Since pickup of physiological artifacts can not be limited by such physical means, the prior art also includes efforts to remove artifacts by signal processing methods. See, e.g., Sahul et al., EKG artifact cancellation from sleep EEG using adaptive filtering, J. Sleep Res., vol. 24A, pp 486; Park et al., 2002, Automated detection and elimination of periodic ECG artifacts in EEG using the energy interval histogram method, IEEE Trans. Biomed. Eng., vol. 49, pp 1526-1533; and Anderer et al., 1999, Artifact processing in computerized analysis of sleep EEG—a review, Neuropsychobiology vol. 40, pp 150-7. However, these signal processing methods can unavoidably alter the EEG signals of interest, for example, by smoothing or limiting high frequency components.
Accordingly, there remains a need in the art for improved systems and methods for limiting or removing spurious signals and artifacts in EEG, and in weak physiological signals generally.